{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 边框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 默认的横纵轴是带数字刻度和边框的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataSet = np.array([[1,1],   # 必须是np.array类型，普通的数组类型不可以\n",
    "          [2,2],\n",
    "          [3,3],\n",
    "          [4,4]])\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)   # If no positional arguments are passed, defaults to (1, 1, 1)或(111)\n",
    "ax.scatter(dataSet[:,0], dataSet[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 去掉边框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axl = plt.subplot(111, frameon=False)\n",
    "axl.scatter(dataSet[:,0], dataSet[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 去掉刻度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axprops = dict(xticks=[], yticks=[])\n",
    "axl = plt.subplot(111, **axprops)\n",
    "axl.scatter(dataSet[:,0], dataSet[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 同时去掉边框和刻度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axprops = dict(xticks=[], yticks=[])\n",
    "axl = plt.subplot(111, frameon=False, **axprops)\n",
    "axl.scatter(dataSet[:,0], dataSet[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 坐标轴加label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 颜色区间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x19e3edad5c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dataSet[:,0], dataSet[:,1])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 设置刻度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "#plt.scatter(dataSet[:,0], dataSet[:,1])\n",
    "ax.set_xticks(range(10))\n",
    "ax.set_xticklabels(\"x labels\", rotation=90)\n",
    "ax.set_yticks(range(6))\n",
    "ax.set_yticklabels(\"y labels\", rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yinahe\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(16,8))\n",
    "ax = fig.add_subplot(2,4,1)\n",
    "ax.scatter(dataSet[:,0], dataSet[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
